{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL-Tutorials/blob/master/5-Others/tutorial_multistock_variant_2.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade multiple stocks in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
        "\n",
        "* This blog is based on our paper: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, presented at NeurIPS 2020: Deep RL Workshop.\n",
        "* Check out medium blog for detailed explanations: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-multiple-stock-trading-7b00763b7530\n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: Dow 30 consituents\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- ASSUMES USING DOCKER, see readme for instructions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Add FinRL to your path. You can of course install it as a pipy package, but this is for development purposes.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mPT0ipYE28wL"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "sys.path.append(\"..\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UoecVLQv81Gb",
        "outputId": "195f9d12-3c9d-42cc-f768-d64265afcc24"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.1.5\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "print(pd.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lPqeTTwoh1hn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "%matplotlib inline\n",
        "from finrl import config\n",
        "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.agents.stablebaselines3.models import DRLAgent\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "\n",
        "from pprint import pprint"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "7b69e991-9ec7-447d-b7df-b3ccbd51bf43"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2009-01-01'"
            ]
          },
          "execution_count": 32,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FUnY8WEfLq3C",
        "outputId": "8298540a-4708-4140-f3cb-2ec76083ff10"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2021-01-01'"
            ]
          },
          "execution_count": 33,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# from config.py end_date is a string\n",
        "config.END_DATE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "4b3d0c81-c731-4c52-db74-d41e5e8e9593"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ]
        }
      ],
      "source": [
        "print(config.DOW_30_TICKER)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "7513b178-3695-475e-f541-e6b198a342a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (90630, 8)\n"
          ]
        }
      ],
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2021-01-01',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "763f7a59-a0e1-4e37-d8d7-c5c4707fbb75"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(90630, 8)"
            ]
          },
          "execution_count": 36,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4hYkeaPiICHS",
        "outputId": "c5911b34-68a9-48af-b7cf-99bf98f3c796"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.782837</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.782837  746015200  AAPL    4\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.657365   10955700   AXP    4\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.655109    7117200   CAT    4\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.505757   40980600  CSCO    4"
            ]
          },
          "execution_count": 37,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Ci1H47t81Gg",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "c89a10a4-6007-4c06-8c0d-d52d0bf5ee0b",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n"
          ]
        }
      ],
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = config.INDICATORS,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "processed = fe.preprocess_data(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "grvhGJJII3Xn",
        "outputId": "f5af4eff-3d98-4a0e-fad4-37bfaafc012c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.782837</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>74.230003</td>\n",
              "      <td>77.300003</td>\n",
              "      <td>73.580002</td>\n",
              "      <td>46.800758</td>\n",
              "      <td>13695900</td>\n",
              "      <td>CVX</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>21.605234</td>\n",
              "      <td>22.060680</td>\n",
              "      <td>20.993229</td>\n",
              "      <td>15.219840</td>\n",
              "      <td>13251037</td>\n",
              "      <td>DD</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>22.760000</td>\n",
              "      <td>24.030001</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>20.597496</td>\n",
              "      <td>9796600</td>\n",
              "      <td>DIS</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>84.019997</td>\n",
              "      <td>87.620003</td>\n",
              "      <td>82.190002</td>\n",
              "      <td>72.315208</td>\n",
              "      <td>14088500</td>\n",
              "      <td>GS</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>23.070000</td>\n",
              "      <td>24.190001</td>\n",
              "      <td>22.959999</td>\n",
              "      <td>17.705078</td>\n",
              "      <td>14902500</td>\n",
              "      <td>HD</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high  ...     cci_30  dx_30  turbulence\n",
              "0  2009-01-02   3.067143   3.251429  ...  66.666667  100.0         0.0\n",
              "1  2009-01-02  18.570000  19.520000  ...  66.666667  100.0         0.0\n",
              "2  2009-01-02  42.799999  45.560001  ...  66.666667  100.0         0.0\n",
              "3  2009-01-02  44.910000  46.980000  ...  66.666667  100.0         0.0\n",
              "4  2009-01-02  16.410000  17.000000  ...  66.666667  100.0         0.0\n",
              "5  2009-01-02  74.230003  77.300003  ...  66.666667  100.0         0.0\n",
              "6  2009-01-02  21.605234  22.060680  ...  66.666667  100.0         0.0\n",
              "7  2009-01-02  22.760000  24.030001  ...  66.666667  100.0         0.0\n",
              "8  2009-01-02  84.019997  87.620003  ...  66.666667  100.0         0.0\n",
              "9  2009-01-02  23.070000  24.190001  ...  66.666667  100.0         0.0\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "execution_count": 39,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "processed.sort_values(['date','tic'],ignore_index=True).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TOhcryx44bb"
      },
      "source": [
        "## Training data split: 2009-01-01 to 2018-12-31\n",
        "## Trade data split: 2019-01-01 to 2020-09-30"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0qaVGjLtgbI",
        "outputId": "53a4113a-4c25-4384-d063-ec805f22923c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "75480\n",
            "15150\n"
          ]
        }
      ],
      "source": [
        "train = data_split(processed, '2009-01-01','2019-01-01')\n",
        "trade = data_split(processed, '2019-01-01','2021-01-01')\n",
        "print(len(train))\n",
        "print(len(trade))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "po20-XQR81Gi",
        "outputId": "9c23148d-b211-4151-e40a-d1f9039bb354"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1628590684615\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "milliseconds = int(round(time.time() * 1000))\n",
        "print(milliseconds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W7ELA8e081Gi"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from gym.utils import seeding\n",
        "import gym\n",
        "from gym import spaces\n",
        "import matplotlib\n",
        "matplotlib.use('Agg')\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "#from stable_baselines3.common import logger\n",
        "\n",
        "\n",
        "class StockTradingEnv(gym.Env):\n",
        "    \"\"\"A stock trading environment for OpenAI gym\"\"\"\n",
        "    metadata = {'render.modes': ['human']}\n",
        "\n",
        "    def __init__(self, \n",
        "                df, \n",
        "                stock_dim,\n",
        "                hmax,                \n",
        "                initial_amount,\n",
        "                buy_cost_pct,\n",
        "                sell_cost_pct,\n",
        "                reward_scaling,\n",
        "                state_space,\n",
        "                action_space,\n",
        "                tech_indicator_list,\n",
        "                turbulence_threshold=None,\n",
        "                risk_indicator_col='turbulence',\n",
        "                make_plots = False, \n",
        "                print_verbosity = 10,\n",
        "                day = 0, \n",
        "                initial=True,\n",
        "                previous_state=[],\n",
        "                model_name = '',\n",
        "                mode='',\n",
        "                iteration=''):\n",
        "        self.day = day\n",
        "        self.df = df\n",
        "        self.stock_dim = stock_dim\n",
        "        self.hmax = hmax\n",
        "        self.initial_amount = initial_amount\n",
        "        self.buy_cost_pct = buy_cost_pct\n",
        "        self.sell_cost_pct = sell_cost_pct\n",
        "        self.reward_scaling = reward_scaling\n",
        "        self.state_space = state_space\n",
        "        self.action_space = action_space\n",
        "        self.tech_indicator_list = tech_indicator_list\n",
        "        self.action_space = spaces.Box(low = -1, high = 1,shape = (self.action_space,)) \n",
        "        self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape = (self.state_space,))\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        self.terminal = False     \n",
        "        self.make_plots = make_plots\n",
        "        self.print_verbosity = print_verbosity\n",
        "        self.turbulence_threshold = turbulence_threshold\n",
        "        self.risk_indicator_col = risk_indicator_col\n",
        "        self.initial = initial\n",
        "        self.previous_state = previous_state\n",
        "        self.model_name=model_name\n",
        "        self.mode=mode \n",
        "        self.iteration=iteration\n",
        "        # initalize state\n",
        "        self.state = self._initiate_state()\n",
        "        \n",
        "        # initialize reward\n",
        "        self.reward = 0\n",
        "        self.turbulence = 0\n",
        "        self.cost = 0\n",
        "        self.trades = 0\n",
        "        self.episode = 0\n",
        "        # memorize all the total balance change\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        self.rewards_memory = []\n",
        "        self.actions_memory=[]\n",
        "        self.date_memory=[self._get_date()]\n",
        "        #self.reset()\n",
        "        self._seed()\n",
        "        \n",
        "\n",
        "\n",
        "    def _sell_stock(self, index, action):\n",
        "        def _do_sell_normal():\n",
        "            if self.state[index+1]>0: \n",
        "                # Sell only if the price is > 0 (no missing data in this particular date)\n",
        "                # perform sell action based on the sign of the action\n",
        "                if self.state[index+self.stock_dim+1] > 0:\n",
        "                    # Sell only if current asset is > 0\n",
        "                    sell_num_shares = min(abs(action),self.state[index+self.stock_dim+1])\n",
        "                    sell_amount = self.state[index+1] * sell_num_shares * (1- self.sell_cost_pct)\n",
        "                    #update balance\n",
        "                    self.state[0] += sell_amount\n",
        "\n",
        "                    self.state[index+self.stock_dim+1] -= sell_num_shares\n",
        "                    self.cost +=self.state[index+1] * sell_num_shares * self.sell_cost_pct\n",
        "                    self.trades+=1\n",
        "                else:\n",
        "                    sell_num_shares = 0\n",
        "            else:\n",
        "                sell_num_shares = 0\n",
        "\n",
        "            return sell_num_shares\n",
        "            \n",
        "        # perform sell action based on the sign of the action\n",
        "        if self.turbulence_threshold is not None:\n",
        "            if self.turbulence>=self.turbulence_threshold:\n",
        "                if self.state[index+1]>0: \n",
        "                    # Sell only if the price is > 0 (no missing data in this particular date)\n",
        "                    # if turbulence goes over threshold, just clear out all positions \n",
        "                    if self.state[index+self.stock_dim+1] > 0:\n",
        "                        # Sell only if current asset is > 0\n",
        "                        sell_num_shares = self.state[index+self.stock_dim+1]\n",
        "                        sell_amount = self.state[index+1]*sell_num_shares* (1- self.sell_cost_pct)\n",
        "                        #update balance\n",
        "                        self.state[0] += sell_amount\n",
        "                        self.state[index+self.stock_dim+1] =0\n",
        "                        self.cost += self.state[index+1]*sell_num_shares* \\\n",
        "                                    self.sell_cost_pct\n",
        "                        self.trades+=1\n",
        "                    else:\n",
        "                        sell_num_shares = 0\n",
        "                else:\n",
        "                    sell_num_shares = 0\n",
        "            else:\n",
        "                sell_num_shares = _do_sell_normal()\n",
        "        else:\n",
        "            sell_num_shares = _do_sell_normal()\n",
        "\n",
        "        return sell_num_shares\n",
        "\n",
        "    \n",
        "    def _buy_stock(self, index, action):\n",
        "\n",
        "        def _do_buy():\n",
        "            if self.state[index+1]>0: \n",
        "                #Buy only if the price is > 0 (no missing data in this particular date)       \n",
        "                available_amount = self.state[0] // self.state[index+1]\n",
        "                # print('available_amount:{}'.format(available_amount))\n",
        "                \n",
        "                #update balance\n",
        "                buy_num_shares = min(available_amount, action)\n",
        "                buy_amount = self.state[index+1] * buy_num_shares * (1+ self.buy_cost_pct)\n",
        "                self.state[0] -= buy_amount\n",
        "\n",
        "                self.state[index+self.stock_dim+1] += buy_num_shares\n",
        "                \n",
        "                self.cost+=self.state[index+1] * buy_num_shares * self.buy_cost_pct\n",
        "                self.trades+=1\n",
        "            else:\n",
        "                buy_num_shares = 0\n",
        "\n",
        "            return buy_num_shares\n",
        "\n",
        "        # perform buy action based on the sign of the action\n",
        "        if self.turbulence_threshold is None:\n",
        "            buy_num_shares = _do_buy()\n",
        "        else:\n",
        "            if self.turbulence< self.turbulence_threshold:\n",
        "                buy_num_shares = _do_buy()\n",
        "            else:\n",
        "                buy_num_shares = 0\n",
        "                pass\n",
        "\n",
        "        return buy_num_shares\n",
        "\n",
        "    def _make_plot(self):\n",
        "        plt.plot(self.asset_memory,'r')\n",
        "        plt.savefig('results/account_value_trade_{}.png'.format(self.episode))\n",
        "        plt.close()\n",
        "\n",
        "    def step(self, actions):\n",
        "        self.terminal = self.day >= len(self.df.index.unique())-1\n",
        "        if self.terminal:\n",
        "            # print(f\"Episode: {self.episode}\")\n",
        "            if self.make_plots:\n",
        "                self._make_plot()            \n",
        "            end_total_asset = self.state[0]+ \\\n",
        "                sum(np.array(self.state[1:(self.stock_dim+1)])*np.array(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]))\n",
        "            df_total_value = pd.DataFrame(self.asset_memory)\n",
        "            tot_reward = self.state[0]+sum(np.array(self.state[1:(self.stock_dim+1)])*np.array(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]))- self.initial_amount \n",
        "            df_total_value.columns = ['account_value']\n",
        "            df_total_value['date'] = self.date_memory\n",
        "            df_total_value['daily_return']=df_total_value['account_value'].pct_change(1)\n",
        "            if df_total_value['daily_return'].std() !=0:\n",
        "                sharpe = (252**0.5)*df_total_value['daily_return'].mean()/ \\\n",
        "                      df_total_value['daily_return'].std()\n",
        "            df_rewards = pd.DataFrame(self.rewards_memory)\n",
        "            df_rewards.columns = ['account_rewards']\n",
        "            df_rewards['date'] = self.date_memory[:-1]\n",
        "            if self.episode % self.print_verbosity == 0:\n",
        "                print(f\"day: {self.day}, episode: {self.episode}\")\n",
        "                print(f\"begin_total_asset: {self.asset_memory[0]:0.2f}\")\n",
        "                print(f\"end_total_asset: {end_total_asset:0.2f}\")\n",
        "                print(f\"total_reward: {tot_reward:0.2f}\")\n",
        "                print(f\"total_cost: {self.cost:0.2f}\")\n",
        "                print(f\"total_trades: {self.trades}\")\n",
        "                if df_total_value['daily_return'].std() != 0:\n",
        "                    print(f\"Sharpe: {sharpe:0.3f}\")\n",
        "                print(\"=================================\")\n",
        "\n",
        "            if (self.model_name!='') and (self.mode!=''):\n",
        "                df_actions = self.save_action_memory()\n",
        "                df_actions.to_csv('results/actions_{}_{}_{}.csv'.format(self.mode,self.model_name, self.iteration))\n",
        "                df_total_value.to_csv('results/account_value_{}_{}_{}.csv'.format(self.mode,self.model_name, self.iteration),index=False)\n",
        "                df_rewards.to_csv('results/account_rewards_{}_{}_{}.csv'.format(self.mode,self.model_name, self.iteration),index=False)\n",
        "                plt.plot(self.asset_memory,'r')\n",
        "                plt.savefig('results/account_value_{}_{}_{}.png'.format(self.mode,self.model_name, self.iteration),index=False)\n",
        "                plt.close()\n",
        "\n",
        "            # Add outputs to logger interface\n",
        "            #logger.record(\"environment/portfolio_value\", end_total_asset)\n",
        "            #logger.record(\"environment/total_reward\", tot_reward)\n",
        "            #logger.record(\"environment/total_reward_pct\", (tot_reward / (end_total_asset - tot_reward)) * 100)\n",
        "            #logger.record(\"environment/total_cost\", self.cost)\n",
        "            #logger.record(\"environment/total_trades\", self.trades)\n",
        "\n",
        "            return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "        else:\n",
        "\n",
        "            actions = actions * self.hmax #actions initially is scaled between 0 to 1\n",
        "            actions = (actions.astype(int)) #convert into integer because we can't by fraction of shares\n",
        "            if self.turbulence_threshold is not None:\n",
        "                if self.turbulence>=self.turbulence_threshold:\n",
        "                    actions=np.array([-self.hmax]*self.stock_dim)\n",
        "            begin_total_asset = self.state[0]+ \\\n",
        "            sum(np.array(self.state[1:(self.stock_dim+1)])*np.array(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]))\n",
        "            #print(\"begin_total_asset:{}\".format(begin_total_asset))\n",
        "            \n",
        "            argsort_actions = np.argsort(actions)\n",
        "            \n",
        "            sell_index = argsort_actions[:np.where(actions < 0)[0].shape[0]]\n",
        "            buy_index = argsort_actions[::-1][:np.where(actions > 0)[0].shape[0]]\n",
        "\n",
        "            for index in sell_index:\n",
        "                # print(f\"Num shares before: {self.state[index+self.stock_dim+1]}\")\n",
        "                # print(f'take sell action before : {actions[index]}')\n",
        "                actions[index] = self._sell_stock(index, actions[index]) * (-1)\n",
        "                # print(f'take sell action after : {actions[index]}')\n",
        "                # print(f\"Num shares after: {self.state[index+self.stock_dim+1]}\")\n",
        "\n",
        "            for index in buy_index:\n",
        "                # print('take buy action: {}'.format(actions[index]))\n",
        "                actions[index] = self._buy_stock(index, actions[index])\n",
        "\n",
        "            self.actions_memory.append(actions)\n",
        "            \n",
        "            #state: s -> s+1\n",
        "            self.day += 1\n",
        "            self.data = self.df.loc[self.day,:]    \n",
        "            if self.turbulence_threshold is not None:     \n",
        "                self.turbulence = self.data[self.risk_indicator_col].values[0]\n",
        "            self.state =  self._update_state()\n",
        "                           \n",
        "            end_total_asset = self.state[0]+ \\\n",
        "            sum(np.array(self.state[1:(self.stock_dim+1)])*np.array(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]))\n",
        "            self.asset_memory.append(end_total_asset)\n",
        "            self.date_memory.append(self._get_date())\n",
        "            self.reward = end_total_asset - begin_total_asset            \n",
        "            self.rewards_memory.append(self.reward)\n",
        "            self.reward = self.reward*self.reward_scaling\n",
        "\n",
        "        return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "    def reset(self):  \n",
        "        #initiate state\n",
        "        self.state = self._initiate_state()\n",
        "        \n",
        "        if self.initial:\n",
        "            self.asset_memory = [self.initial_amount]\n",
        "        else:\n",
        "            previous_total_asset = self.previous_state[0]+ \\\n",
        "            sum(np.array(self.state[1:(self.stock_dim+1)])*np.array(self.previous_state[(self.stock_dim+1):(self.stock_dim*2+1)]))\n",
        "            self.asset_memory = [previous_total_asset]\n",
        "\n",
        "        self.day = 0\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        self.turbulence = 0\n",
        "        self.cost = 0\n",
        "        self.trades = 0\n",
        "        self.terminal = False \n",
        "        # self.iteration=self.iteration\n",
        "        self.rewards_memory = []\n",
        "        self.actions_memory=[]\n",
        "        self.date_memory=[self._get_date()]\n",
        "        \n",
        "        self.episode+=1\n",
        "\n",
        "        return self.state\n",
        "    \n",
        "    def render(self, mode='human',close=False):\n",
        "        return self.state\n",
        "\n",
        "    def _initiate_state(self):\n",
        "        if self.initial:\n",
        "            # For Initial State\n",
        "            if len(self.df.tic.unique())>1:\n",
        "                # for multiple stock\n",
        "                state = [self.initial_amount] + \\\n",
        "                         self.data.close.values.tolist() + \\\n",
        "                         [0]*self.stock_dim  + \\\n",
        "                         sum([self.data[tech].values.tolist() for tech in self.tech_indicator_list ], [])\n",
        "            else:\n",
        "                # for single stock\n",
        "                state = [self.initial_amount] + \\\n",
        "                        [self.data.close] + \\\n",
        "                        [0]*self.stock_dim  + \\\n",
        "                        sum([[self.data[tech]] for tech in self.tech_indicator_list ], [])\n",
        "        else:\n",
        "            #Using Previous State\n",
        "            if len(self.df.tic.unique())>1:\n",
        "                # for multiple stock\n",
        "                state = [self.previous_state[0]] + \\\n",
        "                         self.data.close.values.tolist() + \\\n",
        "                         self.previous_state[(self.stock_dim+1):(self.stock_dim*2+1)]  + \\\n",
        "                         sum([self.data[tech].values.tolist() for tech in self.tech_indicator_list ], [])\n",
        "            else:\n",
        "                # for single stock\n",
        "                state = [self.previous_state[0]] + \\\n",
        "                        [self.data.close] + \\\n",
        "                        self.previous_state[(self.stock_dim+1):(self.stock_dim*2+1)]  + \\\n",
        "                        sum([[self.data[tech]] for tech in self.tech_indicator_list ], [])\n",
        "        return state\n",
        "\n",
        "    def _update_state(self):\n",
        "        if len(self.df.tic.unique())>1:\n",
        "            # for multiple stock\n",
        "            state =  [self.state[0]] + \\\n",
        "                      self.data.close.values.tolist() + \\\n",
        "                      list(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]) + \\\n",
        "                      sum([self.data[tech].values.tolist() for tech in self.tech_indicator_list ], [])\n",
        "\n",
        "        else:\n",
        "            # for single stock\n",
        "            state =  [self.state[0]] + \\\n",
        "                     [self.data.close] + \\\n",
        "                     list(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]) + \\\n",
        "                     sum([[self.data[tech]] for tech in self.tech_indicator_list ], [])\n",
        "                          \n",
        "        return state\n",
        "\n",
        "    def _get_date(self):\n",
        "        if len(self.df.tic.unique())>1:\n",
        "            date = self.data.date.unique()[0]\n",
        "        else:\n",
        "            date = self.data.date\n",
        "        return date\n",
        "\n",
        "    def save_asset_memory(self):\n",
        "        date_list = self.date_memory\n",
        "        asset_list = self.asset_memory\n",
        "        #print(len(date_list))\n",
        "        #print(len(asset_list))\n",
        "        df_account_value = pd.DataFrame({'date':date_list,'account_value':asset_list})\n",
        "        return df_account_value\n",
        "\n",
        "    def save_action_memory(self):\n",
        "        if len(self.df.tic.unique())>1:\n",
        "            # date and close price length must match actions length\n",
        "            date_list = self.date_memory[:-1]\n",
        "            df_date = pd.DataFrame(date_list)\n",
        "            df_date.columns = ['date']\n",
        "            \n",
        "            action_list = self.actions_memory\n",
        "            df_actions = pd.DataFrame(action_list)\n",
        "            df_actions.columns = self.data.tic.values\n",
        "            df_actions.index = df_date.date\n",
        "            #df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
        "        else:\n",
        "            date_list = self.date_memory[:-1]\n",
        "            action_list = self.actions_memory\n",
        "            df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
        "        return df_actions\n",
        "\n",
        "    def _seed(self, seed=None):\n",
        "        self.np_random, seed = seeding.np_random(seed)\n",
        "        return [seed]\n",
        "\n",
        "\n",
        "    def get_sb_env(self):\n",
        "        e = DummyVecEnv([lambda: self])\n",
        "        obs = e.reset()\n",
        "        return e, obs\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3DChHv1I81Gl",
        "outputId": "b083e935-9e85-4a97-f447-36990e5e858a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A stock trading environment for OpenAI gym\n"
          ]
        }
      ],
      "source": [
        "print(StockTradingEnv.__doc__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2zqII8rMIqn",
        "outputId": "8a2c943b-1be4-4b8d-b64f-666e0852b7e6"
      },
      "source": [
        "#### state space\n",
        "The state space of the observation is as follows \n",
        "\n",
        "`start_cash, <owned_shares_of_n_assets>, <<indicator_i_for_asset_j> for j in assets>`\n",
        "\n",
        "indicators are any daily measurement you can achieve. Common ones are 'volume', 'open' 'close' 'high', 'low'.\n",
        "However, you can add these as needed, \n",
        "The feature engineer adds indicators, and you can add your own as well. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "AWyp84Ltto19",
        "outputId": "c9134406-c08a-4c6c-8d0b-701766b264a7"
      },
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-70-bd466b544e74>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m }\n\u001b[1;32m     12\u001b[0m e_train_gym = StockTradingEnv(df = train, cache_indicator_data=True,\n\u001b[0;32m---> 13\u001b[0;31m                               **env_kwargs)\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# e_train_obs = StockTradingEnvV2(df = train, cache_indicator_data=False, **env_kwargs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'cache_indicator_data'"
          ]
        }
      ],
      "source": [
        "information_cols = ['open', 'high', 'low', 'close', 'volume', 'day', 'macd', 'rsi_30', 'cci_30', 'dx_30', 'turbulence']\n",
        "\n",
        "env_kwargs = {\n",
        "    \"hmax\": 5000, \n",
        "    \"daily_reward\": 5,\n",
        "    \"out_of_cash_penalty\": 0, \n",
        "    \"cash_penalty_proportion\": 0.1,\n",
        "    \"daily_information_cols\": information_cols, \n",
        "    \"print_verbosity\": 500, \n",
        "    \n",
        "}\n",
        "e_train_gym = StockTradingEnv(df = train, cache_indicator_data=True,\n",
        "                              **env_kwargs)\n",
        "\n",
        "# e_train_obs = StockTradingEnvV2(df = train, cache_indicator_data=False, **env_kwargs)\n",
        "\n",
        "\n",
        "# e_trade_gym = StockTradingEnvV2(df = train,**env_kwargs)\n",
        "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64EoqOrQjiVf"
      },
      "source": [
        "## Environment for Training\n",
        "There are two available environments. The multiprocessing and the single processing env. \n",
        "Some models won't work with multiprocessing. \n",
        "\n",
        "```python\n",
        "# single processing\n",
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "\n",
        "\n",
        "#multiprocessing\n",
        "env_train, _ = e_train_gym.get_multiproc_env(n = <n_cores>)\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "xwSvvPjutpqS",
        "jupyter": {
          "outputs_hidden": true
        },
        "outputId": "0b9f9e2f-3bec-4c45-8f2e-b81213c62907"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "using 24 cores\n"
          ]
        }
      ],
      "source": [
        "# for this example, let's do multiprocessing with n_cores-2\n",
        "\n",
        "import multiprocessing\n",
        "\n",
        "n_cores = multiprocessing.cpu_count() - 2\n",
        "n_cores = 24\n",
        "print(f\"using {n_cores} cores\")\n",
        "\n",
        "# \n",
        "e_train_gym.print_verbosity = 500\n",
        "env_train, _ = e_train_gym.get_multiproc_env(n = n_cores)\n",
        "# env_train, _ = e_train_gym.get_sb_env()\n",
        "\n",
        "env_train_obs, _ = e_train_gym.get_sb_env()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "364PsqckttcQ"
      },
      "outputs": [],
      "source": [
        "agent = DRLAgent(env = env_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxOiPqYd81Gm",
        "outputId": "16af4a3a-30f7-4174-9899-c329bb8a6bb9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'n_steps': 2048, 'ent_coef': 0.01, 'learning_rate': 0.00025, 'batch_size': 64}\n"
          ]
        }
      ],
      "source": [
        "print(config.PPO_PARAMS)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDmqOyF9h1iz"
      },
      "source": [
        "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uijiWgkuh1jB"
      },
      "source": [
        "### Model 1: A2C\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5WZLwk9y81Gm",
        "outputId": "40c55a50-e8b6-4843-e4d8-642397e0c51a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'n_steps': 256, 'ent_coef': 0.01, 'learning_rate': 1e-05, 'batch_size': 256, 'gamma': 0.99}\n"
          ]
        }
      ],
      "source": [
        "from torch.nn import Softsign, ReLU\n",
        "ppo_params ={'n_steps': 256, \n",
        "             'ent_coef': 0.01, \n",
        "             'learning_rate': 0.00001, \n",
        "             'batch_size': 256, \n",
        "            'gamma': 0.99}\n",
        "\n",
        "policy_kwargs = {\n",
        "#     \"activation_fn\": ReLU,\n",
        "    \"net_arch\": [1024, 1024, 1024,  1024], \n",
        "#     \"squash_output\": True\n",
        "}\n",
        "\n",
        "model = agent.get_model(\"ppo\",  model_kwargs = ppo_params, policy_kwargs = policy_kwargs, verbose = 0)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "f8x-2elg81Gm",
        "outputId": "55ba89e4-4528-421a-df92-91bc49d9537a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/callbacks.py:337: UserWarning: Training and eval env are not of the same type<stable_baselines3.common.vec_env.subproc_vec_env.SubprocVecEnv object at 0x7f8a2c865bd0> != <stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv object at 0x7f8a2c865750>\n",
            "  warnings.warn(\"Training and eval env are not of the same type\" f\"{self.training_env} != {self.eval_env}\")\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-5c6fc4b00200>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0mtb_log_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'cashbuffer_1_16_longrun'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m             \u001b[0mn_eval_episodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m             reset_num_timesteps = True)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/ppo/ppo.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m             \u001b[0meval_log_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meval_log_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m             \u001b[0mreset_num_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset_num_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/on_policy_algorithm.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m             \u001b[0mcontinue_training\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect_rollouts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrollout_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_rollout_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcontinue_training\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/on_policy_algorithm.py\u001b[0m in \u001b[0;36mcollect_rollouts\u001b[0;34m(self, env, callback, rollout_buffer, n_rollout_steps)\u001b[0m\n\u001b[1;32m    173\u001b[0m                 \u001b[0mclipped_actions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m             \u001b[0mnew_obs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclipped_actions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/base_vec_env.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \"\"\"\n\u001b[1;32m    161\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/subproc_vec_env.py\u001b[0m in \u001b[0;36mstep_wait\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mVecEnvStepReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mremote\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mremote\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremotes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaiting\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0mobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrews\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/subproc_vec_env.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mVecEnvStepReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mremote\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mremote\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremotes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaiting\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0mobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrews\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/multiprocessing/connection.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_readable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recv_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_ForkingPickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetbuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_recv_bytes\u001b[0;34m(self, maxsize)\u001b[0m\n\u001b[1;32m    405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_recv_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m         \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstruct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munpack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"!i\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmaxsize\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mmaxsize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_recv\u001b[0;34m(self, size, read)\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0mremaining\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mremaining\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m             \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    380\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "model.learn(total_timesteps = 10000000, \n",
        "            eval_env = env_train_obs, \n",
        "            eval_freq = 1000,\n",
        "            log_interval = 1, \n",
        "            tb_log_name = 'cashbuffer_1_16_longrun',\n",
        "            n_eval_episodes = 1,\n",
        "            reset_num_timesteps = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-g4JlJGF81Gm"
      },
      "outputs": [],
      "source": [
        "model.save(\"quicksave_ppo_dow_1_17.model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "efwBi84ch1jE"
      },
      "outputs": [],
      "source": [
        "data_turbulence = processed[(processed.date<'2019-01-01') & (processed.date>='2009-01-01')]\n",
        "insample_turbulence = data_turbulence.drop_duplicates(subset=['date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHZMBpSqh1jG",
        "outputId": "7f4b9bd7-ba24-4ead-c0ee-8b90d43390e9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    2516.000000\n",
              "mean       33.278197\n",
              "std        33.999900\n",
              "min         0.000000\n",
              "25%        15.233724\n",
              "50%        25.167089\n",
              "75%        39.290041\n",
              "max       332.848895\n",
              "Name: turbulence, dtype: float64"
            ]
          },
          "execution_count": 52,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "insample_turbulence.turbulence.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yuwDPkV9h1jL"
      },
      "outputs": [],
      "source": [
        "turbulence_threshold = np.quantile(insample_turbulence.turbulence.values,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwoz_7VSh1jO",
        "outputId": "975b3cb4-0a8b-46d2-86ca-4e7f9e900d66"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "332.84889494854804"
            ]
          },
          "execution_count": 54,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "turbulence_threshold"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5mmgQF_h1jQ"
      },
      "source": [
        "### Trade\n",
        "\n",
        "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2018-12 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
        "\n",
        "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eLOnL5eYh1jR"
      },
      "outputs": [],
      "source": [
        "trade = data_split(processed, '2019-01-01','2021-01-01')\n",
        "env_kwargs = {\n",
        "    \"hmax\": 5000, \n",
        "    \"daily_reward\": 5,\n",
        "    \"out_of_cash_penalty\": 0, \n",
        "    \"cash_penalty_proportion\": 0.1,\n",
        "    \"daily_information_cols\": information_cols, \n",
        "    \"print_verbosity\": 50, \n",
        "    \"random_start\": False,\n",
        "    \"cache_indicator_data\": False\n",
        "    \n",
        "}\n",
        "e_trade_gym = StockTradingEnvV2(df = trade,**env_kwargs)\n",
        "env_trade, obs_trade = e_trade_gym.get_sb_env()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AtvH4LJa81Gn",
        "outputId": "7d46ffe9-7618-46dd-a7bc-eedf5d6c537a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "505\n"
          ]
        }
      ],
      "source": [
        "print(len(e_trade_gym.dates))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "VDa3v9jw81Gn",
        "outputId": "eaa7b209-cb4f-4da0-ce13-53fe5f0ac3dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EPISODE|STEPS|TERMINAL_REASON|TOT_ASSETS|TERMINAL_REWARD_unsc|CASH_PCT  \n",
            "   1|  49|update         |$1026908  |$3903.39  |38.52%    \n",
            "   1|  99|update         |$1009031  |$-9975.56 |11.49%    \n",
            "   1| 116|CASH SHORTAGE  |$1039050  |$0.00     |0.53%     \n"
          ]
        },
        {
          "ename": "AttributeError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-59-d0dc7b0c3132>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m df_account_value, df_actions = DRLAgent.DRL_prediction(model=model,\n\u001b[0;32m----> 2\u001b[0;31m                         environment=e_trade_gym)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/finrl/drl_agents/stablebaselines3/models.py\u001b[0m in \u001b[0;36mDRL_prediction\u001b[0;34m(model, environment)\u001b[0m\n\u001b[1;32m     75\u001b[0m             \u001b[0;31m#account_memory = test_env.env_method(method_name=\"save_asset_memory\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0;31m#actions_memory = test_env.env_method(method_name=\"save_action_memory\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m             \u001b[0mtest_obs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_env\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menvironment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m               \u001b[0maccount_memory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_env\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"save_asset_memory\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/base_vec_env.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \"\"\"\n\u001b[1;32m    161\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/dummy_vec_env.py\u001b[0m in \u001b[0;36mstep_wait\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0menv_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx] = self.envs[env_idx].step(\n\u001b[0;32m---> 44\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m             )\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuf_dones\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-42-e2c3e35be85a>\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    264\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mspend\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcosts\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mcoh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m                 return self.return_terminal(reason = 'CASH SHORTAGE',\n\u001b[0;32m--> 266\u001b[0;31m                     \u001b[0mreward\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mout_of_cash_penalty\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m                 )\n\u001b[1;32m    268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-42-e2c3e35be85a>\u001b[0m in \u001b[0;36mreturn_terminal\u001b[0;34m(self, reason, reward)\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0;31m# Add outputs to logger interface\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0mreward_pct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccount_information\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total_assets'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitial_amount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"environment/total_reward_pct\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mreward_pct\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"environment/daily_trades\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum_trades\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"environment/completed_steps\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'stable_baselines3.common.logger' has no attribute 'record'"
          ]
        }
      ],
      "source": [
        "df_account_value, df_actions = DRLAgent.DRL_prediction(model=model,\n",
        "                        environment=e_trade_gym)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERxw3KqLkcP4",
        "outputId": "cbb465c9-38dc-4d88-e79a-6ae29025164b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(504, 5)"
            ]
          },
          "execution_count": 218,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_account_value.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "2yRkNguY5yvp",
        "outputId": "53ec139f-88e7-4291-cf11-8e6766184265"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cash</th>\n",
              "      <th>asset_value</th>\n",
              "      <th>total_assets</th>\n",
              "      <th>reward</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2019-01-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>954713.504851</td>\n",
              "      <td>43767.481172</td>\n",
              "      <td>9.984810e+05</td>\n",
              "      <td>-1514.013976</td>\n",
              "      <td>2019-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>921059.195094</td>\n",
              "      <td>79889.003263</td>\n",
              "      <td>1.000948e+06</td>\n",
              "      <td>2477.212333</td>\n",
              "      <td>2019-01-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>898000.392462</td>\n",
              "      <td>103023.607858</td>\n",
              "      <td>1.001024e+06</td>\n",
              "      <td>90.801963</td>\n",
              "      <td>2019-01-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>833324.488711</td>\n",
              "      <td>168756.699053</td>\n",
              "      <td>1.002081e+06</td>\n",
              "      <td>1077.187444</td>\n",
              "      <td>2019-01-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>840406.267319</td>\n",
              "      <td>161930.011111</td>\n",
              "      <td>1.002336e+06</td>\n",
              "      <td>280.090666</td>\n",
              "      <td>2019-01-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>831378.560262</td>\n",
              "      <td>171642.142407</td>\n",
              "      <td>1.003021e+06</td>\n",
              "      <td>714.424239</td>\n",
              "      <td>2019-01-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>867326.480855</td>\n",
              "      <td>135434.564406</td>\n",
              "      <td>1.002761e+06</td>\n",
              "      <td>-224.657408</td>\n",
              "      <td>2019-01-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>878547.979759</td>\n",
              "      <td>122862.990393</td>\n",
              "      <td>1.001411e+06</td>\n",
              "      <td>-1310.075109</td>\n",
              "      <td>2019-01-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>870158.138198</td>\n",
              "      <td>131876.909758</td>\n",
              "      <td>1.002035e+06</td>\n",
              "      <td>669.077804</td>\n",
              "      <td>2019-01-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>856458.704083</td>\n",
              "      <td>145370.092272</td>\n",
              "      <td>1.001829e+06</td>\n",
              "      <td>-156.251601</td>\n",
              "      <td>2019-01-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>854157.486324</td>\n",
              "      <td>148713.826010</td>\n",
              "      <td>1.002871e+06</td>\n",
              "      <td>1097.515979</td>\n",
              "      <td>2019-01-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>839601.817357</td>\n",
              "      <td>165297.845181</td>\n",
              "      <td>1.004900e+06</td>\n",
              "      <td>2088.350203</td>\n",
              "      <td>2019-01-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>849032.293973</td>\n",
              "      <td>153013.178698</td>\n",
              "      <td>1.002045e+06</td>\n",
              "      <td>-2789.189867</td>\n",
              "      <td>2019-01-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>826812.059251</td>\n",
              "      <td>175033.196129</td>\n",
              "      <td>1.001845e+06</td>\n",
              "      <td>-130.217290</td>\n",
              "      <td>2019-01-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>822931.385002</td>\n",
              "      <td>179368.842528</td>\n",
              "      <td>1.002300e+06</td>\n",
              "      <td>529.972150</td>\n",
              "      <td>2019-01-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>819012.663201</td>\n",
              "      <td>182538.202776</td>\n",
              "      <td>1.001551e+06</td>\n",
              "      <td>-669.361553</td>\n",
              "      <td>2019-01-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>859765.086042</td>\n",
              "      <td>138741.876525</td>\n",
              "      <td>9.985070e+05</td>\n",
              "      <td>-2958.903411</td>\n",
              "      <td>2019-01-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>838687.334916</td>\n",
              "      <td>160414.818279</td>\n",
              "      <td>9.991022e+05</td>\n",
              "      <td>685.190629</td>\n",
              "      <td>2019-01-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>823536.097213</td>\n",
              "      <td>177945.884349</td>\n",
              "      <td>1.001482e+06</td>\n",
              "      <td>2474.828367</td>\n",
              "      <td>2019-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>800760.844097</td>\n",
              "      <td>201358.731284</td>\n",
              "      <td>1.002120e+06</td>\n",
              "      <td>737.593820</td>\n",
              "      <td>2019-02-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>814279.596213</td>\n",
              "      <td>188486.322468</td>\n",
              "      <td>1.002766e+06</td>\n",
              "      <td>751.343299</td>\n",
              "      <td>2019-02-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>796369.333423</td>\n",
              "      <td>206569.777298</td>\n",
              "      <td>1.002939e+06</td>\n",
              "      <td>283.192040</td>\n",
              "      <td>2019-02-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>792632.237544</td>\n",
              "      <td>211101.433276</td>\n",
              "      <td>1.003734e+06</td>\n",
              "      <td>909.560098</td>\n",
              "      <td>2019-02-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>786157.833973</td>\n",
              "      <td>217246.977425</td>\n",
              "      <td>1.003405e+06</td>\n",
              "      <td>-208.859422</td>\n",
              "      <td>2019-02-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>790178.413957</td>\n",
              "      <td>211202.195735</td>\n",
              "      <td>1.001381e+06</td>\n",
              "      <td>-1899.201705</td>\n",
              "      <td>2019-02-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>799778.110107</td>\n",
              "      <td>200565.736393</td>\n",
              "      <td>1.000344e+06</td>\n",
              "      <td>-906.763193</td>\n",
              "      <td>2019-02-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>796121.992657</td>\n",
              "      <td>204046.529562</td>\n",
              "      <td>1.000169e+06</td>\n",
              "      <td>-40.324281</td>\n",
              "      <td>2019-02-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>813972.286893</td>\n",
              "      <td>189151.741155</td>\n",
              "      <td>1.003124e+06</td>\n",
              "      <td>3095.505829</td>\n",
              "      <td>2019-02-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>810867.923737</td>\n",
              "      <td>193225.370881</td>\n",
              "      <td>1.004093e+06</td>\n",
              "      <td>1114.266570</td>\n",
              "      <td>2019-02-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>793680.022911</td>\n",
              "      <td>210080.989358</td>\n",
              "      <td>1.003761e+06</td>\n",
              "      <td>-182.282349</td>\n",
              "      <td>2019-02-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>809270.093136</td>\n",
              "      <td>197783.498745</td>\n",
              "      <td>1.007054e+06</td>\n",
              "      <td>3447.579612</td>\n",
              "      <td>2019-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>826521.057034</td>\n",
              "      <td>180337.971824</td>\n",
              "      <td>1.006859e+06</td>\n",
              "      <td>-34.563024</td>\n",
              "      <td>2019-02-20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>818195.886153</td>\n",
              "      <td>188849.075671</td>\n",
              "      <td>1.007045e+06</td>\n",
              "      <td>350.932967</td>\n",
              "      <td>2019-02-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>816446.804054</td>\n",
              "      <td>189885.963016</td>\n",
              "      <td>1.006333e+06</td>\n",
              "      <td>-542.194754</td>\n",
              "      <td>2019-02-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>810573.116220</td>\n",
              "      <td>197228.686269</td>\n",
              "      <td>1.007802e+06</td>\n",
              "      <td>1644.035418</td>\n",
              "      <td>2019-02-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>845948.733139</td>\n",
              "      <td>163362.649064</td>\n",
              "      <td>1.009311e+06</td>\n",
              "      <td>1689.579714</td>\n",
              "      <td>2019-02-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>834277.109748</td>\n",
              "      <td>174318.068505</td>\n",
              "      <td>1.008595e+06</td>\n",
              "      <td>-531.203949</td>\n",
              "      <td>2019-02-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>835626.556278</td>\n",
              "      <td>173289.144002</td>\n",
              "      <td>1.008916e+06</td>\n",
              "      <td>510.522026</td>\n",
              "      <td>2019-02-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>827381.096711</td>\n",
              "      <td>180606.421457</td>\n",
              "      <td>1.007988e+06</td>\n",
              "      <td>-733.182112</td>\n",
              "      <td>2019-03-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>834862.521546</td>\n",
              "      <td>173375.739433</td>\n",
              "      <td>1.008238e+06</td>\n",
              "      <td>450.742811</td>\n",
              "      <td>2019-03-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>833989.597892</td>\n",
              "      <td>173597.881959</td>\n",
              "      <td>1.007587e+06</td>\n",
              "      <td>-445.781127</td>\n",
              "      <td>2019-03-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>851661.198089</td>\n",
              "      <td>155163.822747</td>\n",
              "      <td>1.006825e+06</td>\n",
              "      <td>-552.459016</td>\n",
              "      <td>2019-03-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>842768.861599</td>\n",
              "      <td>163183.093706</td>\n",
              "      <td>1.005952e+06</td>\n",
              "      <td>-658.065531</td>\n",
              "      <td>2019-03-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>859257.804590</td>\n",
              "      <td>145160.719170</td>\n",
              "      <td>1.004419e+06</td>\n",
              "      <td>-1313.431544</td>\n",
              "      <td>2019-03-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>842499.228840</td>\n",
              "      <td>161518.655134</td>\n",
              "      <td>1.004018e+06</td>\n",
              "      <td>-175.639786</td>\n",
              "      <td>2019-03-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>832258.858765</td>\n",
              "      <td>174556.424948</td>\n",
              "      <td>1.006815e+06</td>\n",
              "      <td>3027.399739</td>\n",
              "      <td>2019-03-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>849127.556444</td>\n",
              "      <td>157492.064512</td>\n",
              "      <td>1.006620e+06</td>\n",
              "      <td>39.337242</td>\n",
              "      <td>2019-03-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>878209.089380</td>\n",
              "      <td>128930.002442</td>\n",
              "      <td>1.007139e+06</td>\n",
              "      <td>759.470866</td>\n",
              "      <td>2019-03-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>892091.795957</td>\n",
              "      <td>114888.245246</td>\n",
              "      <td>1.006980e+06</td>\n",
              "      <td>85.949381</td>\n",
              "      <td>2019-03-15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              cash    asset_value  total_assets       reward        date\n",
              "0   1000000.000000       0.000000  1.000000e+06     0.000000  2019-01-03\n",
              "1    954713.504851   43767.481172  9.984810e+05 -1514.013976  2019-01-04\n",
              "2    921059.195094   79889.003263  1.000948e+06  2477.212333  2019-01-07\n",
              "3    898000.392462  103023.607858  1.001024e+06    90.801963  2019-01-08\n",
              "4    833324.488711  168756.699053  1.002081e+06  1077.187444  2019-01-09\n",
              "5    840406.267319  161930.011111  1.002336e+06   280.090666  2019-01-10\n",
              "6    831378.560262  171642.142407  1.003021e+06   714.424239  2019-01-11\n",
              "7    867326.480855  135434.564406  1.002761e+06  -224.657408  2019-01-14\n",
              "8    878547.979759  122862.990393  1.001411e+06 -1310.075109  2019-01-15\n",
              "9    870158.138198  131876.909758  1.002035e+06   669.077804  2019-01-16\n",
              "10   856458.704083  145370.092272  1.001829e+06  -156.251601  2019-01-17\n",
              "11   854157.486324  148713.826010  1.002871e+06  1097.515979  2019-01-18\n",
              "12   839601.817357  165297.845181  1.004900e+06  2088.350203  2019-01-22\n",
              "13   849032.293973  153013.178698  1.002045e+06 -2789.189867  2019-01-23\n",
              "14   826812.059251  175033.196129  1.001845e+06  -130.217290  2019-01-24\n",
              "15   822931.385002  179368.842528  1.002300e+06   529.972150  2019-01-25\n",
              "16   819012.663201  182538.202776  1.001551e+06  -669.361553  2019-01-28\n",
              "17   859765.086042  138741.876525  9.985070e+05 -2958.903411  2019-01-29\n",
              "18   838687.334916  160414.818279  9.991022e+05   685.190629  2019-01-30\n",
              "19   823536.097213  177945.884349  1.001482e+06  2474.828367  2019-01-31\n",
              "20   800760.844097  201358.731284  1.002120e+06   737.593820  2019-02-01\n",
              "21   814279.596213  188486.322468  1.002766e+06   751.343299  2019-02-04\n",
              "22   796369.333423  206569.777298  1.002939e+06   283.192040  2019-02-05\n",
              "23   792632.237544  211101.433276  1.003734e+06   909.560098  2019-02-06\n",
              "24   786157.833973  217246.977425  1.003405e+06  -208.859422  2019-02-07\n",
              "25   790178.413957  211202.195735  1.001381e+06 -1899.201705  2019-02-08\n",
              "26   799778.110107  200565.736393  1.000344e+06  -906.763193  2019-02-11\n",
              "27   796121.992657  204046.529562  1.000169e+06   -40.324281  2019-02-12\n",
              "28   813972.286893  189151.741155  1.003124e+06  3095.505829  2019-02-13\n",
              "29   810867.923737  193225.370881  1.004093e+06  1114.266570  2019-02-14\n",
              "30   793680.022911  210080.989358  1.003761e+06  -182.282349  2019-02-15\n",
              "31   809270.093136  197783.498745  1.007054e+06  3447.579612  2019-02-19\n",
              "32   826521.057034  180337.971824  1.006859e+06   -34.563024  2019-02-20\n",
              "33   818195.886153  188849.075671  1.007045e+06   350.932967  2019-02-21\n",
              "34   816446.804054  189885.963016  1.006333e+06  -542.194754  2019-02-22\n",
              "35   810573.116220  197228.686269  1.007802e+06  1644.035418  2019-02-25\n",
              "36   845948.733139  163362.649064  1.009311e+06  1689.579714  2019-02-26\n",
              "37   834277.109748  174318.068505  1.008595e+06  -531.203949  2019-02-27\n",
              "38   835626.556278  173289.144002  1.008916e+06   510.522026  2019-02-28\n",
              "39   827381.096711  180606.421457  1.007988e+06  -733.182112  2019-03-01\n",
              "40   834862.521546  173375.739433  1.008238e+06   450.742811  2019-03-04\n",
              "41   833989.597892  173597.881959  1.007587e+06  -445.781127  2019-03-05\n",
              "42   851661.198089  155163.822747  1.006825e+06  -552.459016  2019-03-06\n",
              "43   842768.861599  163183.093706  1.005952e+06  -658.065531  2019-03-07\n",
              "44   859257.804590  145160.719170  1.004419e+06 -1313.431544  2019-03-08\n",
              "45   842499.228840  161518.655134  1.004018e+06  -175.639786  2019-03-11\n",
              "46   832258.858765  174556.424948  1.006815e+06  3027.399739  2019-03-12\n",
              "47   849127.556444  157492.064512  1.006620e+06    39.337242  2019-03-13\n",
              "48   878209.089380  128930.002442  1.007139e+06   759.470866  2019-03-14\n",
              "49   892091.795957  114888.245246  1.006980e+06    85.949381  2019-03-15"
            ]
          },
          "execution_count": 187,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_account_value.head(50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtest Our Strategy\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "outputId": "1053083a-d74c-48b0-a623-de33282e2fff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "annual return:  -0.31429838045686775\n",
            "sharpe ratio:  -0.03215222673502538\n",
            "Annual return         -0.007905\n",
            "Cumulative returns    -0.015717\n",
            "Annual volatility      0.097907\n",
            "Sharpe ratio          -0.032152\n",
            "Calmar ratio          -0.050407\n",
            "Stability              0.353511\n",
            "Max drawdown          -0.156831\n",
            "Omega ratio            0.993158\n",
            "Sortino ratio         -0.044483\n",
            "Skew                  -0.219349\n",
            "Kurtosis               9.495171\n",
            "Tail ratio             0.946898\n",
            "Daily value at risk   -0.012348\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = backtest_stats(account_value=df_account_value, value_col_name = 'total_assets')\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lKRGftSS7pNM",
        "outputId": "4f77cef2-3934-444a-cacc-4ed8f94514ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==============Compare to DJIA===========\n",
            "annual return:  -0.31429838045686775\n",
            "sharpe ratio:  -0.03215222673502538\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 8)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-03</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-12-30</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>23</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>-0.791%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>-1.572%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>9.791%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>-0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>-0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-15.683%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>0.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>-0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>9.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-1.235%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>-0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15.68</td>\n",
              "      <td>2019-04-23</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.64</td>\n",
              "      <td>2019-01-18</td>\n",
              "      <td>2019-01-28</td>\n",
              "      <td>2019-02-15</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.52</td>\n",
              "      <td>2019-02-25</td>\n",
              "      <td>2019-03-08</td>\n",
              "      <td>2019-04-23</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.16</td>\n",
              "      <td>2019-01-10</td>\n",
              "      <td>2019-01-14</td>\n",
              "      <td>2019-01-18</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.07</td>\n",
              "      <td>2019-02-15</td>\n",
              "      <td>2019-02-21</td>\n",
              "      <td>2019-02-22</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.6/site-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
            "  'interesting times.', UserWarning)\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "backtest_plot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = '2019-01-01',\n",
        "             baseline_end = '2021-01-01', value_col_name = 'total_assets')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlLT9_5WN478"
      },
      "source": [
        "<a id='6.3'></a>\n",
        "## 7.3 Baseline Stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YktexHcqh1jc",
        "outputId": "38566531-a3a0-4705-db30-d437e8f8fc73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 8)\n",
            "Annual return          0.144674\n",
            "Cumulative returns     0.310981\n",
            "Annual volatility      0.274619\n",
            "Sharpe ratio           0.631418\n",
            "Calmar ratio           0.390102\n",
            "Stability              0.116677\n",
            "Max drawdown          -0.370862\n",
            "Omega ratio            1.149365\n",
            "Sortino ratio          0.870084\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.860710\n",
            "Daily value at risk   -0.033911\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(\"==============Get Baseline Stats===========\")\n",
        "\n",
        "baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\", \n",
        "        start = '2019-01-01',\n",
        "        end = '2021-01-01')\n",
        "\n",
        "baseline_stats = backtest_stats(baseline_df, value_col_name = 'close')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A6W2J57ch1j9"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "uijiWgkuh1jB"
      ],
      "name": "tutorial_multistock_variant_2.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.12"
    },
    "pycharm": {
      "stem_cell": {
        "cell_type": "raw",
        "metadata": {
          "collapsed": false
        },
        "source": []
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
